Timezone: »

 
Retrieval-based Controllable Molecule Generation
Jack Wang · Weili Nie · Zhuoran Qiao · Chaowei Xiao · Richard Baraniuk · Anima Anandkumar

@
in AI for Science: Progress and Promises »
Event URL: https://openreview.net/forum?id=SM2AX5pz2wy »

Generating new molecules with specified chemical and biological properties via generative models has emerged as a promising direction for drug discovery. However, existing methods require extensive training/fine-tuning with a large dataset, often unavailable in real-world generation tasks. In this work, we propose a new retrieval-based framework for controllable molecule generation. We use a small set of exemplar molecules, i.e., those that (partially) satisfy the design criteria, to steer the pre-trained generative model towards synthesizing molecules that satisfy the given design criteria. We design a retrieval mechanism that retrieves and fuses the exemplar molecules with the input molecule, which is trained by a new self-supervised objective that predicts the nearest neighbor of the input molecule. We also propose an iterative refinement process to dynamically update the generated molecules and retrieval database for better generalization. Our approach is agnostic to the choice of generative models and requires no task-specific fine-tuning. On various tasks ranging from simple design criteria to a challenging real-world scenario for designing lead compounds that bind to the SARS-CoV-2 main protease, we demonstrate our approach extrapolates well beyond the retrieval database, and achieves better performance and wider applicability than previous methods.

Keywords: Information Retrieval · Drug Discovery · controllable molecule generation

Author Information

Jack Wang (Rice University)
Weili Nie (NVIDIA)
Zhuoran Qiao (Caltech)

I'm a Ph.D. student at Miller Group, Caltech CCE. I'm working on developing deep learning methods with prior physical information for studying challenging problems in molecular electronic structures and dynamics.

Chaowei Xiao (ASU/NVIDIA)

I am Chaowei Xiao, a third year PhD student in CSE Department, University of Michigan, Ann Arbor. My advisor is Professor Mingyan Liu . I obtained my bachelor's degree in School of Software from Tsinghua University in 2015, advised by Professor Yunhao Liu, Professor Zheng Yang and Dr. Lei Yang. I was also a visiting student at UC Berkeley in 2018, advised by Professor Dawn Song and Professor Bo Li. My research interest includes adversarial machine learning.

Richard Baraniuk (Rice University)
Anima Anandkumar (NVIDIA / Caltech)

More from the Same Authors